• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.113-116
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• 2006

Successful field test results for detection of buried undersea HVDC by magnetic vector measurements are discussed. Further study will be focused on practical utilization.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.137-142
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• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.107-112
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• 2024

In this paper, we propose a method to detect abnormal power usage conditions in domestic franchise convenience stores, by detecting cases where the temperature of the refrigeration or freezer equipment operates outside the normal range and classifying detailed abnormal situations. Compared to normal data, abnormal data is very small, and the amount of data varies depending on the type of abnormality, leading to a data imbalance issue. The proposed method employs a hierarchical structure that combines a time series classification algorithm with kNN, addressing the data imbalance problem and enabling classification using relatively small amounts of data. In this paper, we conducted an experiment by independently constructing our own dataset to validate the proposed methodology.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.435-442
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• 2018

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.819-820
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• 2016

Electrical power distribution is consists of high voltage, low voltage and motor control center(MCC). Motor control centers involves turning the motor on and off, it is configured electronic over current relay to detect a motor overcurrent flows. Existing electronic over current relay detects electrical fault such as overcurrent, undercurrent, phase sequence, negative sequence current, current unbalance and earth fault. However, it is difficult to detect mechanical fault such as locked rotor, motor stator and rotor and bearing fault. In this paper, we propose a condition monitoring and fault diagnosis system for electrical and mechanical fault detection of rotating machinery. The proposed system is designed with signal input and control part, system interface part and data acquisition board for condition monitoring and fault diagnosis, it was possible to detect electrical fault and mechanical fault through measurement and control of insulation resistance, locked rotor, MC counter and bearing temperature.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.449-458
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• 2011

Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. Recently government agency installed COD (chemical oxygen demand) auto-monitering machines in river. In this article we provide an outlier detection algorithm using R based on the time series intervention model that detects some outlier values among those COD time series values generated from an auto-monitering machine. Through this algorithm using R, we can achieve an automatic algorithm that does not need manual intervention in each step, and that can further be used in simulation study.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.224-226
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• 2022

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.54-56
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• 2021

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.